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AlexNet: Image Classification
AlexNet is a convolutional neural network model introduced in 2012 by Alex Krizhevsky and his team. It achieved remarkable success in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and is considered a milestone that sparked the modern deep learning revolution. AlexNet consists of five convolutional layers and three fully connected layers, utilizing the ReLU activation function to speed up training and employing Dropout to prevent overfitting. The model also leveraged GPU parallel computing to significantly improve training speed. AlexNet’s design greatly improved the accuracy of image classification tasks, marking the widespread adoption of convolutional neural networks in computer vision.
Source model
- Input shape: 224x224
- Number of parameters: 58.27M
- Model size: 233.08M
- Output shape: 1x1000
Source model repository: AlexNet
Performance Reference
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Inference & Model Conversion
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License
Source Model: BSD-3-CLAUSE
Deployable Model: APLUX-MODEL-FARM-LICENSE